The global market size for AI in ecommerce is estimated at $9 billion in 2025, and is anticipated to reach $64 billion by 2034, reflecting a compound annual growth rate (CAGR) of nearly 25% over the next decade. Although this growth trajectory signals immense opportunity, it also magnifies the peril of poor data quality when it comes to planning and executing AI initiatives.
Like other transformative technologies (e.g. smartphones and cloud computing), the momentum behind AI brings a mixture of excitement and dread. This is especially true for ecommerce businesses because AI simultaneously promises huge productivity gains that come with major upheaval. Moreover, the industry is racing to launch AI-enabled tools in response to competitive urgency.
Because businesses are feeling the dual burden of strategic necessity and market pressure, it’s tempting to prioritize rapid rollout over comprehensive data validation to deliver results quickly. The problem with this approach is that the accuracy and reliability of AI-enabled tools are directly dependent on the quality of the underlying data.
Rapid market growth amplifies the scope and scale of this risk. When ecommerce businesses increase the quantity of systems reliant on substandard datasets, any issues with data quality — inconsistencies, errors and gaps — are multiplied across an ever-growing footprint, which makes the deficiencies more impactful and consequential. Incomplete and inaccurate data also can disseminate through interconnected systems, resulting in negative impacts to operations, the customer experience and broader business outcomes.
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Recognize the Risks of Substandard Data Quality
AI models trained on poor-quality data can generate outputs that don’t accurately reflect operational efficacy, market trend analysis, product performance or customer behavior, so the consequences of this disconnect can be substantial and far-reaching for your business. Faulty AI inputs can compromise the data analytics that leadership needs to make sound business decisions with confidence.
When it comes to forecasting, messy data can skew demand predictions, resulting in inventory mismanagement, over- or understock situations and lost sales. Order and availability errors can cause shipping mistakes that compromise the customer experience and strain your support resources. Investments in resource allocation, new product launches and marketing spend will underperform because they’re based on flawed assumptions.
Inaccurate or incomplete data also can lead to violations of data protection laws (e.g. CCPA or GDPR), which can expose your business to sanctions, fines and other legal action. Additionally, poor data quality can be problematic during audits, increasing your risk of regulatory scrutiny and reputational harm.
Compromised data can increase your risk of breach, because gaps can weaken your security controls, making it easier for attackers to infiltrate your systems. Poor data management also can result in data leaks that invite unauthorized access or unintentional exposure of sensitive information. Eventually, persistent data security and compliance issues will hinder your ability to scale, enter new markets or attract partners.
When you’re looking to grow your AI program, scaling challenges (e.g. integration headaches and bias/fairness issues) can derail reliability, effectiveness and confidence in your initiative.
Integrating AI into existing ecommerce systems can reveal inconsistencies across data sources, such as conflicting time stamps, outdated labels, and mismatched customer IDs. This can lead to unreliable insights and operational chaos. Disjointed data management and integration errors will waste resources, delay projects and increase costs for ongoing troubleshooting and manual intervention.
Poor data quality also can introduce and amplify biases within your AI models. Biased AI outcomes can result in discriminatory decisions and recommendations, exposing your business to reputational harm.
Ultimately, lack of trust in your AI outputs will compromise your business agility because your stakeholders may be hesitant to use the technology and revert to manual processes.
Avoid Data Quality Pitfalls
Some ecommerce businesses are investing heavily in AI without proper due diligence because they believe their data quality is sufficient. The negative impacts of this shortcut often surface only after deployment, when AI-enabled tools start causing noticeable problems.
In many cases, AI struggles are tied to unreliable data and are often mistaken for algorithmic issues or attributed to the AI vendor’s shortcomings when, in fact, underlying data issues are the root cause. This common misdiagnosis extends delays in remediation that lead to repeated project failures. Organizations that find themselves in this situation will eventually be forced to accept that their resources have been wasted.
For ecommerce businesses, data is often dispersed across multiple platforms (e.g. CRM, ERP, supply chain systems and web analytics). This reality creates challenges for building a unified, real-time view of customer and product performance. To address this issue, some businesses are implementing a centralized data platform, integrating systems with middleware and APIs, and establishing data governance and quality controls.
Because gen AI models require large, context-rich datasets, fragmentation issues are particularly troublesome. When these models are trained on poor-quality data, they’re prone to generating hallucinations — outputs that appear to be plausible but are incorrect — which can mislead both ecommerce customers and employees. Performing regular updates, relying on verified sources and investing the time and effort needed to ensure datasets are complete and accurate helps minimize this risk.
Elevate Data Quality to a Strategic AI Imperative
Poor data quality can derail your ecommerce AI and gen AI initiatives because it undermines the foundation on which these systems are built. The damage is often hidden until they cause significant harm to your business. This is why proactively prioritizing data quality management is essential for any organization seeking success with their AI investments.
Investing in governance frameworks, automated data cleaning pipelines and regular audits will help you maintain data integrity. Integrating dispersed data sources and standardizing formats will build a unified source of truth for your AI models.
Implementing ongoing data quality monitoring systemizes your success, and treating training datasets as fluid assets that require regular review and updates helps keep your initiatives on track.
At the end of the day, data quality is a shared organizational responsibility, not just an IT concern.
Sachin Sharma serves as the Chief Product Officer and Head of Professional Services at Kibo Commerce. In this capacity, he oversees product strategy, management, user experience and documentation, while also leading the professional services team responsible for client implementations and partner enablement. Prior to joining Kibo in 2018, Sharma was part of Vista Equity Partners’ operating arm, collaborating with executive teams across the firm’s portfolio to enhance go-to-market effectiveness and operational efficiency. He holds an A.B. in economics and philosophy from the University of Chicago.
